Update app.py

app.py

@@ -1,13 +1,112 @@
 import gradio as gr
 from transformers import PreTrainedTokenizerFast, AutoConfig
-from safetensors.torch import load_model  # Import load_model for safetensors
+from safetensors.torch import load_model
 import torch
-from EnhancedTransformerModel import EnhancedTransformerModel  # Custom model class
+import math
+import torch.nn as nn
+
+
+# --- Define Snowflake4CausalLM ---
+class FusedQKVAttention(nn.Module):
+    def __init__(self, d_model, num_heads):
+        super().__init__()
+        self.d_model = d_model
+        self.num_heads = num_heads
+        self.head_dim = d_model // num_heads
+        self.qkv_proj = nn.Linear(d_model, 3 * d_model)
+        self.wo = nn.Linear(d_model, d_model)
+        nn.init.xavier_uniform_(self.qkv_proj.weight)
+        nn.init.xavier_uniform_(self.wo.weight)
+        nn.init.zeros_(self.qkv_proj.bias)
+        nn.init.zeros_(self.wo.bias)
+
+    def forward(self, x, attention_mask=None):
+        batch_size, seq_len, _ = x.shape
+        qkv = self.qkv_proj(x).reshape(batch_size, seq_len, 3, self.num_heads, self.head_dim)
+        qkv = qkv.permute(2, 0, 3, 1, 4)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+        attention_scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+        if attention_mask is not None:
+            attention_mask = attention_mask.unsqueeze(1).unsqueeze(2)
+            attention_scores = attention_scores.masked_fill(attention_mask == 0, float('-inf'))
+        attention_weights = torch.softmax(attention_scores, dim=-1)
+        context = torch.matmul(attention_weights, v)
+        context = context.transpose(1, 2).reshape(batch_size, seq_len, self.d_model)
+        return self.wo(context)
+
+
+class EnhancedFeedForward(nn.Module):
+    def __init__(self, d_model, ff_dim, dropout=0.1):
+        super().__init__()
+        self.linear1 = nn.Linear(d_model, ff_dim)
+        self.dropout1 = nn.Dropout(dropout)
+        self.linear2 = nn.Linear(ff_dim, d_model)
+        self.dropout2 = nn.Dropout(dropout)
+        self.activation = nn.GELU()
+        nn.init.xavier_uniform_(self.linear1.weight)
+        nn.init.xavier_uniform_(self.linear2.weight)
+        nn.init.zeros_(self.linear1.bias)
+        nn.init.zeros_(self.linear2.bias)
+
+    def forward(self, x):
+        return self.dropout2(self.linear2(self.dropout1(self.activation(self.linear1(x)))))
+
+
+class EnhancedTransformerBlock(nn.Module):
+    def __init__(self, d_model, num_heads, ff_dim, dropout=0.1):
+        super().__init__()
+        self.attention = FusedQKVAttention(d_model, num_heads)
+        self.norm1 = nn.LayerNorm(d_model, eps=1e-6)
+        self.dropout1 = nn.Dropout(dropout)
+        self.feed_forward = EnhancedFeedForward(d_model, ff_dim, dropout)
+        self.norm2 = nn.LayerNorm(d_model, eps=1e-6)
+        self.dropout2 = nn.Dropout(dropout)
+
+    def forward(self, x, attention_mask=None):
+        attn_input = self.norm1(x)
+        attn_output = self.attention(attn_input, attention_mask)
+        x = x + self.dropout1(attn_output)
+        ff_input = self.norm2(x)
+        ff_output = self.feed_forward(ff_input)
+        x = x + self.dropout2(ff_output)
+        return x
+
+
+class Snowflake4CausalLM(nn.Module):
+    def __init__(self, vocab_size, max_seq_length, d_model, num_heads, num_layers, ff_dim, dropout=0.1):
+        super().__init__()
+        self.embedding = nn.Embedding(vocab_size, d_model)
+        self.pos_encoding = nn.Parameter(torch.zeros(1, max_seq_length, d_model))
+        position = torch.arange(max_seq_length).unsqueeze(1).float()
+        div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model))
+        pos_enc = torch.zeros(1, max_seq_length, d_model)
+        pos_enc[0, :, 0::2] = torch.sin(position * div_term)
+        pos_enc[0, :, 1::2] = torch.cos(position * div_term)
+        self.pos_encoding.data = pos_enc.data
+        self.layers = nn.ModuleList([
+            EnhancedTransformerBlock(d_model, num_heads, ff_dim, dropout)
+            for _ in range(num_layers)
+        ])
+        self.final_norm = nn.LayerNorm(d_model, eps=1e-6)
+        self.dropout = nn.Dropout(dropout)
+        self.fc_out = nn.Linear(d_model, vocab_size)
+        self.fc_out.weight = self.embedding.weight
+        nn.init.normal_(self.embedding.weight, mean=0, std=0.02)
+
+    def forward(self, input_ids, attention_mask=None):
+        seq_length = input_ids.size(1)
+        x = self.embedding(input_ids) + self.pos_encoding[:, :seq_length, :]
+        x = self.dropout(x)
+        for layer in self.layers:
+            x = layer(x, attention_mask)
+        x = self.final_norm(x)
+        return self.fc_out(x)
+
 
 # --- Load Model and Tokenizer ---
-MODEL_PATH = "model.safetensors"  # Path to the safetensors model file
-CONFIG_PATH = "config.json"       # Path to the model configuration
-TOKENIZER_PATH = "tokenizer"      # Path to the tokenizer directory
+MODEL_PATH = "model.safetensors"
+CONFIG_PATH = "config.json"
+TOKENIZER_PATH = "tokenizer"
 
 # Load configuration
 config = AutoConfig.from_pretrained(CONFIG_PATH)
@@ -16,7 +115,7 @@ config = AutoConfig.from_pretrained(CONFIG_PATH)
 tokenizer = PreTrainedTokenizerFast.from_pretrained(TOKENIZER_PATH)
 
 # Initialize the custom model
-model = EnhancedTransformerModel(
+model = Snowflake4CausalLM(
     vocab_size=config.vocab_size,
     max_seq_length=config.max_position_embeddings,
     d_model=config.hidden_size,
@@ -32,26 +131,22 @@ model.load_state_dict(state_dict)
 model.eval()
 model.to("cuda" if torch.cuda.is_available() else "cpu")
 
+
 # --- Inference Function ---
 def generate_text(prompt, max_length=50):
-    """
-    Generate text based on the input prompt using the trained model.
-    """
-    # Tokenize the input prompt
     inputs = tokenizer(prompt, return_tensors="pt", truncation=True, padding=True, max_length=384)
     input_ids = inputs["input_ids"].to(model.device)
     attention_mask = inputs["attention_mask"].to(model.device)
 
-    # Generate output tokens
     with torch.no_grad():
         outputs = model(input_ids, attention_mask)
-        logits = outputs[:, -1, :]  # Get the logits for the last token
-        next_token = torch.argmax(logits, dim=-1)  # Greedy decoding
+        logits = outputs[:, -1, :]
+        next_token = torch.argmax(logits, dim=-1)
 
-    # Decode the generated token
     generated_text = tokenizer.decode(next_token, skip_special_tokens=True)
     return generated_text
 
+
 # --- Gradio Interface ---
 with gr.Blocks() as demo:
     gr.Markdown("# Snowflake-G0-stable Language Model")
@@ -68,5 +163,4 @@ with gr.Blocks() as demo:
 
     submit_button.click(on_submit, inputs=input_prompt, outputs=output_text)
 
-# Launch the app
 demo.launch()